The PCR-GLOBWB (PCRaster Global Water Balance; https://globalhydrology.nl/research/models/pcr-globwb-2-0/) hydrology model is a state-of-the-art, open-source hydrological modeling tool that has become highly valuable in water-related research, particularly in the contexts of climate and hydrology (https://github.com/UU-Hydro/PCR-GLOBWB_model). This model is designed to simulate the terrestrial hydrological cycle comprehensively on a global or regional scale. One of the unique features of PCR-GLOBWB is its ability to incorporate human water use, including domestic, industrial, and irrigation water demands. This capability allows researchers to better assess and understand the impact of anthropogenic activities on water systems—something that most traditional hydrology models lack.
PCR-GLOBWB provides a robust simulation of various hydrological processes, including surface water dynamics, streamflow, groundwater, and even fossil groundwater. By including interactions between groundwater and surface water, it provides a holistic understanding of the hydrological cycle. It integrates a range of components such as rainfall, evaporation, soil moisture, runoff, and discharge into rivers and streams. This comprehensive modeling framework is crucial in today’s context of increasing anthropogenic impacts, as it allows for more accurate projections and understanding of how human activities influence natural water systems.
The model’s inclusion of human water usage adds a critical layer to its analysis, enabling better simulation of scenarios involving irrigation, household consumption, and industrial needs. These factors are particularly important when assessing water scarcity, water availability, and stress on water systems in response to climate change and population growth. The incorporation of human factors into the model makes it uniquely powerful for examining scenarios of future water availability under different socio-economic and climate conditions. This approach enhances the accuracy of predictions, providing essential insights into water resource management, policy-making, and sustainability efforts.
Written in Python and built with the functionality of the PCRaster framework, PCR-GLOBWB is accessible and adaptable for researchers and practitioners alike. The use of Python not only ensures that the model is versatile and easy to modify, but it also allows for integration with other data analysis and modeling tools. As an open-source model, it promotes collaboration among the scientific community, encouraging improvements, customizations, and shared developments that can benefit various hydrological research applications.
ALICE-LAB utilizes the PCR-GLOBWB model to assess global terrestrial hydrology and explore critical variables such as terrestrial water storage, groundwater, and streamflow. By doing so, we aim to address the complex challenges posed by climate change and human interventions on water resources. The model’s ability to represent both natural processes and human water use, such as irrigation and industrial withdrawals, allows us to better capture the intricacies of water systems under diverse pressures.
Furthermore, we assimilate satellite data, such as those from the Gravity Recovery and Climate Experiment (GRACE) mission, into the model to enhance its performance. The integration of GRACE data has led to pronounced improvements in the accuracy of groundwater storage estimates, providing a more reliable picture of water availability and trends over time. This approach helps us generate insights that are not only scientifically robust but also crucial for informing sustainable water management practices and policies under scenarios of increasing climate variability and human demands.
References:
Tangdamrongsub, N., 2023. Comparative Analysis of Global Terrestrial Water Storage Simulations: Assessing CABLE, Noah-MP, PCR-GLOBWB, and GLDAS Performances during the GRACE and GRACE-FO Era. Water 15, 2456. https://doi.org/10.3390/w15132456
Tangdamrongsub, N., Hwang, C., Borak, J.S., Prabnakorn, S., Han, J., 2021. Optimizing GRACE/GRACE-FO data and a priori hydrological knowledge for improved global terrestial water storage component estimates. Journal of Hydrology 598, 126463. https://doi.org/10.1016/j.jhydrol.2021.126463
Tangdamrongsub, N., Šprlák, M., 2021. The Assessment of Hydrologic- and Flood-Induced Land Deformation in Data-Sparse Regions Using GRACE/GRACE-FO Data Assimilation. Remote Sensing 13, 235. https://doi.org/10.3390/rs13020235
Tangdamrongsub, N., Steele-Dunne, S.C., Gunter, B.C., Ditmar, P.G., Sutanudjaja, E.H., Sun, Y., Xia, T., Wang, Z., 2017. Improving estimates of water resources in a semi-arid region by assimilating GRACE data into the PCR-GLOBWB hydrological model. Hydrology and Earth System Sciences 21, 2053–2074. https://doi.org/10.5194/hess-21-2053-2017
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